library(tidyverse)
library(janitor)
library(broom)
library(readxl)
library(jsonlite)
library(gprofiler2)
theme_set(theme_bw())
set.seed(666)gvc_agora_opentargets
Setup environment
Read and prep data
GVC
Genes within 1Mb window of (each side of?) GVC loci from Fanny:
gvc <- read_xlsx("GVC_1Mb_comparison_050224.xlsx") %>%
clean_names() %>%
separate(gene_id, c("gene_id", "version")) %>%
select(-version, -agora_nominated_list, -opentarget_info)
gvcgvc.genes <- gvc %>% distinct(gene_id, .keep_all = TRUE) %>% select(gene_id, gene_symbol) %>% arrange(gene_symbol)
gvc.genesAgora
Alzheimer’s disease gene prioritization scores from Agora (see also related journal article):
ago1 <- read_json("syn25741025.overall_scores.json", simplifyVector = TRUE) %>% as_tibble()
ago1Alzheimer’s disease genes (AMPAD Agora) from Fanny:
ago2 <- read_csv("AMPAD_agora_032124_gene-list.csv")
ago2ago <- ago1 %>% filter(hgnc_symbol %in% ago2$`Gene Symbol`)OpenTargets
Alzheimer’s disease gene prioritization scores from OpenTargets:
ot <- read_tsv("OT-MONDO_0004975-associated-targets-6_4_2024-v24_03.tsv", show_col_types = FALSE, na = "No data")
#ot <- read_tsv("OT-MONDO_0004975-associated-targets-9_19_2024-v24_09.tsv", show_col_types = FALSE, na = "No data")
otAdd Ensembl Gene IDs (WTF!):
otcols <- colnames(ot)
otensg <- gconvert(
query = ot$symbol,
organism = "hsapiens",
target= "ENSG",
mthreshold = Inf,
filter_na = TRUE) %>%
mutate(input_number = as.character(input_number)) %>%
left_join(ot %>% rownames_to_column(var = "input_number"), by = "input_number") %>%
select(ensembl_gene_id = target, otcols)
otensgOverlaps between GVC, Agora, and OpenTargets genes
x = list(
"GVC" = gvc.genes$gene_id,
"Agora" = ago$ensembl_gene_id,
"OpenTargets" = otensg$ensembl_gene_id
)library(VennDiagram)
grid.newpage()
v <- venn.diagram(
x,
fill = c("#FF0000", "#00FF00", "#0000FF"),
filename = NULL)
grid.draw(v)
p <- get.venn.partitions(x)
pPerform ORA of genes in overlaps
GVC ∩ Agora ∩ OpenTargets
genes <- p %>%
filter(..set.. == "GVC∩Agora∩OpenTargets") %>%
unnest(..values..) %>%
select(gene_id = ..values..) %>%
left_join(ago, by = join_by(gene_id == ensembl_gene_id)) %>%
left_join(otensg, by = join_by(gene_id == ensembl_gene_id)) %>%
sample_frac(1L) %>% # randomize row order before arranging
select(gene_id, symbol, genetics_score, otGeneticsPortal, globalScore, target_risk_score, multi_omics_score) %>%
arrange(desc(genetics_score), desc(otGeneticsPortal))
genesquery <- genes %>% distinct(gene_id) %>% pull(gene_id)
gostres <- gost(query = query,
organism = "hsapiens",
domain_scope = "annotated",
exclude_iea = TRUE,
ordered_query = TRUE,
significant = TRUE,
user_threshold = 0.005,
correction_method = "fdr")
gostres$result %>% select(term_name, term_id, source, everything())gostplot(gostres, capped = FALSE, interactive = TRUE)# save overlap gene ids for later
overlap_gene_ids <- queryGVC ∩ Agora
genes <- p %>%
filter(..set.. %in% c("GVC∩Agora∩OpenTargets", "(GVC∩Agora)∖(OpenTargets)")) %>%
unnest(..values..) %>%
select(gene_id = ..values..) %>%
left_join(ago, by = join_by(gene_id == ensembl_gene_id)) %>%
left_join(otensg, by = join_by(gene_id == ensembl_gene_id)) %>%
sample_frac(1L) %>% # randomize row order before arranging
select(gene_id, symbol, genetics_score, otGeneticsPortal, globalScore, target_risk_score, multi_omics_score) %>%
arrange(desc(genetics_score), desc(otGeneticsPortal))
genesquery <- genes %>% distinct(gene_id) %>% pull(gene_id)
gostres <- gost(query = query,
organism = "hsapiens",
domain_scope = "annotated",
exclude_iea = TRUE,
ordered_query = TRUE,
significant = TRUE,
user_threshold = 0.005,
correction_method = "fdr")
gostres$result %>% select(term_name, term_id, source, everything())gostplot(gostres, capped = FALSE, interactive = TRUE)GVC ∩ OpenTargets
genes <- p %>%
filter(..set.. %in% c("GVC∩Agora∩OpenTargets", "(GVC∩OpenTargets)∖(Agora)")) %>%
unnest(..values..) %>%
select(gene_id = ..values..) %>%
left_join(ago, by = join_by(gene_id == ensembl_gene_id)) %>%
left_join(otensg, by = join_by(gene_id == ensembl_gene_id)) %>%
sample_frac(1L) %>% # randomize row order before arranging
select(gene_id, symbol, genetics_score, otGeneticsPortal, globalScore, target_risk_score, multi_omics_score) %>%
arrange(desc(genetics_score), desc(otGeneticsPortal))
genesquery <- genes %>% distinct(gene_id) %>% pull(gene_id)
gostres <- gost(query = query,
organism = "hsapiens",
domain_scope = "annotated",
exclude_iea = TRUE,
ordered_query = TRUE,
significant = TRUE,
user_threshold = 0.005,
correction_method = "fdr")
gostres$result %>% select(term_name, term_id, source, everything())gostplot(gostres, capped = FALSE, interactive = TRUE)Agora ∩ OpenTargets
genes <- p %>%
filter(..set.. %in% c("GVC∩Agora∩OpenTargets", "(Agora∩OpenTargets)∖(GVC)")) %>%
unnest(..values..) %>%
select(gene_id = ..values..) %>%
left_join(ago, by = join_by(gene_id == ensembl_gene_id)) %>%
left_join(otensg, by = join_by(gene_id == ensembl_gene_id)) %>%
sample_frac(1L) %>% # randomize row order before arranging
select(gene_id, symbol, genetics_score, otGeneticsPortal, globalScore, target_risk_score, multi_omics_score) %>%
arrange(desc(genetics_score), desc(otGeneticsPortal))
genesquery <- genes %>% distinct(gene_id) %>% pull(gene_id)
gostres <- gost(query = query,
organism = "hsapiens",
domain_scope = "annotated",
exclude_iea = TRUE,
ordered_query = TRUE,
significant = TRUE,
user_threshold = 0.005,
correction_method = "fdr")
gostres$result %>% select(term_name, term_id, source, everything())gostplot(gostres, capped = FALSE, interactive = TRUE)(GVC ∩ Agora) ∪ (GVC ∩ OpenTargets) ∪ (Agora ∩ OpenTargets)
genes <- p %>%
filter(..set.. %in% c("GVC∩Agora∩OpenTargets", "(GVC∩Agora)∖(OpenTargets)", "(GVC∩OpenTargets)∖(Agora)", "(Agora∩OpenTargets)∖(GVC)")) %>%
unnest(..values..) %>%
select(gene_id = ..values..) %>%
left_join(ago, by = join_by(gene_id == ensembl_gene_id)) %>%
left_join(otensg, by = join_by(gene_id == ensembl_gene_id)) %>%
sample_frac(1L) %>% # randomize row order before arranging
select(gene_id, symbol, genetics_score, otGeneticsPortal, globalScore, target_risk_score, multi_omics_score) %>%
arrange(desc(genetics_score), desc(otGeneticsPortal))
genesquery <- genes %>% distinct(gene_id) %>% pull(gene_id)
gostres <- gost(query = query,
organism = "hsapiens",
domain_scope = "annotated",
exclude_iea = TRUE,
ordered_query = TRUE,
significant = TRUE,
user_threshold = 0.005,
correction_method = "fdr")
gostres$result %>% select(term_name, term_id, source, everything())gostplot(gostres, capped = FALSE, interactive = TRUE)(Agora ∩ OpenTargets) ∖ (GVC)
genes <- p %>%
filter(..set.. == "(Agora∩OpenTargets)∖(GVC)") %>%
unnest(..values..) %>%
select(gene_id = ..values..) %>%
left_join(ago, by = join_by(gene_id == ensembl_gene_id)) %>%
left_join(otensg, by = join_by(gene_id == ensembl_gene_id)) %>%
sample_frac(1L) %>% # randomize row order before arranging
select(gene_id, symbol, genetics_score, otGeneticsPortal, globalScore, target_risk_score, multi_omics_score) %>%
arrange(desc(genetics_score), desc(otGeneticsPortal))
genesquery <- genes %>% distinct(gene_id) %>% pull(gene_id)
gostres <- gost(query = query,
organism = "hsapiens",
domain_scope = "annotated",
exclude_iea = TRUE,
ordered_query = TRUE,
significant = TRUE,
user_threshold = 0.005,
correction_method = "fdr")
gostres$result %>% select(term_name, term_id, source, everything())gostplot(gostres, capped = FALSE, interactive = TRUE)(GVC ∩ OpenTargets) ∖ (Agora)
genes <- p %>%
filter(..set.. == "(GVC∩OpenTargets)∖(Agora)") %>%
unnest(..values..) %>%
select(gene_id = ..values..) %>%
left_join(ago, by = join_by(gene_id == ensembl_gene_id)) %>%
left_join(otensg, by = join_by(gene_id == ensembl_gene_id)) %>%
sample_frac(1L) %>% # randomize row order before arranging
select(gene_id, symbol, genetics_score, otGeneticsPortal, globalScore, target_risk_score, multi_omics_score) %>%
arrange(desc(genetics_score), desc(otGeneticsPortal))
genesquery <- genes %>% distinct(gene_id) %>% pull(gene_id)
gostres <- gost(query = query,
organism = "hsapiens",
domain_scope = "annotated",
exclude_iea = TRUE,
ordered_query = TRUE,
significant = TRUE,
user_threshold = 0.005,
correction_method = "fdr")
gostres$result %>% select(term_name, term_id, source, everything())gostplot(gostres, capped = FALSE, interactive = TRUE)(OpenTargets) ∖ (GVC ∪ Agora)
genes <- p %>%
filter(..set.. == "(OpenTargets)∖(GVC∪Agora)") %>%
unnest(..values..) %>%
select(gene_id = ..values..) %>%
left_join(ago, by = join_by(gene_id == ensembl_gene_id)) %>%
left_join(otensg, by = join_by(gene_id == ensembl_gene_id)) %>%
sample_frac(1L) %>% # randomize row order before arranging
select(gene_id, symbol, genetics_score, otGeneticsPortal, globalScore, target_risk_score, multi_omics_score) %>%
arrange(desc(genetics_score), desc(otGeneticsPortal))
genesquery <- genes %>% distinct(gene_id) %>% pull(gene_id)
gostres <- gost(query = query,
organism = "hsapiens",
domain_scope = "annotated",
exclude_iea = TRUE,
ordered_query = TRUE,
significant = TRUE,
user_threshold = 0.005,
correction_method = "fdr")
gostres$result %>% select(term_name, term_id, source, everything())gostplot(gostres, capped = FALSE, interactive = TRUE)(GVC ∩ Agora) ∖ (OpenTargets)
genes <- p %>%
filter(..set.. == "(GVC∩Agora)∖(OpenTargets)") %>%
unnest(..values..) %>%
select(gene_id = ..values..) %>%
left_join(ago, by = join_by(gene_id == ensembl_gene_id)) %>%
left_join(otensg, by = join_by(gene_id == ensembl_gene_id)) %>%
sample_frac(1L) %>% # randomize row order before arranging
select(gene_id, symbol, genetics_score, otGeneticsPortal, globalScore, target_risk_score, multi_omics_score) %>%
arrange(desc(genetics_score), desc(otGeneticsPortal))
genesquery <- genes %>% distinct(gene_id) %>% pull(gene_id)
gostres <- gost(query = query,
organism = "hsapiens",
domain_scope = "annotated",
exclude_iea = TRUE,
ordered_query = TRUE,
significant = TRUE,
user_threshold = 0.005,
correction_method = "fdr")
gostres$result %>% select(term_name, term_id, source, everything())gostplot(gostres, capped = FALSE, interactive = TRUE)(Agora) ∖ (GVC ∪ OpenTargets)
genes <- p %>%
filter(..set.. == "(Agora)∖(GVC∪OpenTargets)") %>%
unnest(..values..) %>%
select(gene_id = ..values..) %>%
left_join(ago, by = join_by(gene_id == ensembl_gene_id)) %>%
left_join(otensg, by = join_by(gene_id == ensembl_gene_id)) %>%
sample_frac(1L) %>% # randomize row order before arranging
select(gene_id, symbol, genetics_score, otGeneticsPortal, globalScore, target_risk_score, multi_omics_score) %>%
arrange(desc(genetics_score), desc(otGeneticsPortal))
genesquery <- genes %>% distinct(gene_id) %>% pull(gene_id)
gostres <- gost(query = query,
organism = "hsapiens",
domain_scope = "annotated",
exclude_iea = TRUE,
ordered_query = TRUE,
significant = TRUE,
user_threshold = 0.005,
correction_method = "fdr")
gostres$result %>% select(term_name, term_id, source, everything())gostplot(gostres, capped = FALSE, interactive = TRUE)(GVC) ∖ (Agora ∪ OpenTargets)
genes <- p %>%
filter(..set.. == "(GVC)∖(Agora∪OpenTargets)") %>%
unnest(..values..) %>%
select(gene_id = ..values..) %>%
left_join(ago, by = join_by(gene_id == ensembl_gene_id)) %>%
left_join(otensg, by = join_by(gene_id == ensembl_gene_id)) %>%
sample_frac(1L) %>% # randomize row order before arranging
select(gene_id, symbol, genetics_score, otGeneticsPortal, globalScore, target_risk_score, multi_omics_score) %>%
arrange(desc(genetics_score), desc(otGeneticsPortal))
genesquery <- genes %>% distinct(gene_id) %>% pull(gene_id)
gostres <- gost(query = query,
organism = "hsapiens",
domain_scope = "annotated",
exclude_iea = TRUE,
ordered_query = TRUE,
significant = TRUE,
user_threshold = 0.005,
correction_method = "fdr")
gostres$result %>% select(term_name, term_id, source, everything())gostplot(gostres, capped = FALSE, interactive = TRUE)Perform ORA of GVC genes sorted by Agora or OpenTargets scores
GVC genes sorted by Agora’s genetics_score
Arrange by Agora’s genetics_score and OpenTargets’ otGeneticsPortal:
d1 <- gvc.genes %>%
left_join(ago, by = join_by(gene_id == ensembl_gene_id)) %>%
left_join(otensg, by = join_by(gene_id == ensembl_gene_id)) %>%
sample_frac(1L) %>% # randomize row order before arranging
arrange(desc(genetics_score), desc(otGeneticsPortal)) %>%
select(-c(symbol, hgnc_symbol)) %>%
select(gene_id, gene_symbol, genetics_score, otGeneticsPortal, everything())
d1query <- d1 %>% distinct(gene_id) %>% pull(gene_id)
gostres <- gost(query = query,
organism = "hsapiens",
domain_scope = "annotated",
exclude_iea = TRUE,
ordered_query = TRUE,
significant = TRUE,
user_threshold = 0.005,
correction_method = "fdr")
gostres$result %>% select(term_name, term_id, source, everything())gostplot(gostres, capped = FALSE, interactive = TRUE)GVC genes sorted by OpenTargets’ otGeneticsPortal
Arrange by OpenTargets’ otGeneticsPortal and Agora’s genetics_score:
d2 <- gvc.genes %>%
left_join(ago, by = join_by(gene_id == ensembl_gene_id)) %>%
left_join(otensg, by = join_by(gene_id == ensembl_gene_id)) %>%
sample_frac(1L) %>% # randomize row order before arranging
arrange(desc(otGeneticsPortal), desc(genetics_score)) %>%
select(-c(symbol, hgnc_symbol)) %>%
select(gene_id, gene_symbol, otGeneticsPortal, genetics_score, everything())
d2query <- d2 %>% distinct(gene_id) %>% pull(gene_id)
gostres <- gost(query = query,
organism = "hsapiens",
domain_scope = "annotated",
exclude_iea = TRUE,
ordered_query = TRUE,
significant = TRUE,
user_threshold = 0.005,
correction_method = "fdr")
gostres$result %>% select(term_name, term_id, source, everything())gostplot(gostres, capped = FALSE, interactive = TRUE)GVC genes sorted by Agora’s target_risk_score
Arrange by Agora’s target_risk_score and OpenTargets’ globalScore:
d3 <- gvc.genes %>%
left_join(ago, by = join_by(gene_id == ensembl_gene_id)) %>%
left_join(otensg, by = join_by(gene_id == ensembl_gene_id)) %>%
sample_frac(1L) %>% # randomize row order before arranging
arrange(desc(target_risk_score), desc(globalScore)) %>%
select(-c(symbol, hgnc_symbol)) %>%
select(gene_id, gene_symbol, target_risk_score, globalScore, everything())
d3query <- d3 %>% distinct(gene_id) %>% pull(gene_id)
gostres <- gost(query = query,
organism = "hsapiens",
domain_scope = "annotated",
exclude_iea = TRUE,
ordered_query = TRUE,
significant = TRUE,
user_threshold = 0.005,
correction_method = "fdr")
gostres$result %>% select(term_name, term_id, source, everything())gostplot(gostres, capped = FALSE, interactive = TRUE)GVC genes sorted by OpenTargets’ globalScore
Arrange by OpenTargets’ globalScore and Agora’s target_risk_score:
d4 <- gvc.genes %>%
left_join(ago, by = join_by(gene_id == ensembl_gene_id)) %>%
left_join(otensg, by = join_by(gene_id == ensembl_gene_id)) %>%
sample_frac(1L) %>% # randomize row order before arranging
arrange(desc(globalScore), desc(target_risk_score)) %>%
select(-c(symbol, hgnc_symbol)) %>%
select(gene_id, gene_symbol, globalScore, target_risk_score, everything())
d4query <- d4 %>% distinct(gene_id) %>% pull(gene_id)
gostres <- gost(query = query,
organism = "hsapiens",
domain_scope = "annotated",
exclude_iea = TRUE,
ordered_query = TRUE,
significant = TRUE,
user_threshold = 0.005,
correction_method = "fdr")
gostres$result %>% select(term_name, term_id, source, everything())gostplot(gostres, capped = FALSE, interactive = TRUE)Correlation of Agora and OpenTargets scores (GVC genes only)
d.cor <- gvc.genes %>%
left_join(ago, by = join_by(gene_id == ensembl_gene_id)) %>%
left_join(otensg, by = join_by(gene_id == ensembl_gene_id))d.cor %>% nrow()[1] 1345
d.cor %>% drop_na(genetics_score, otGeneticsPortal) %>% nrow()[1] 56
d.cor %>% drop_na(genetics_score, otGeneticsPortal) %>%
summarize(cor = tidy(cor.test(genetics_score, otGeneticsPortal, method="kendall"))) %>%
unnest(cor)d.cor %>% nrow()[1] 1345
d.cor %>% drop_na(target_risk_score, globalScore) %>% nrow()[1] 75
d.cor %>% drop_na(target_risk_score, globalScore) %>%
summarize(cor = tidy(cor.test(target_risk_score, globalScore, method="kendall"))) %>%
unnest(cor)Correlation of Agora and OpenTargets scores (all genes)
d.cor <- ago %>%
left_join(otensg, by = "ensembl_gene_id")d.cor %>% nrow()[1] 926
d.cor %>% drop_na(genetics_score, otGeneticsPortal) %>% nrow()[1] 75
d.cor %>% drop_na(genetics_score, otGeneticsPortal) %>%
summarize(cor = tidy(cor.test(genetics_score, otGeneticsPortal, method="kendall"))) %>%
unnest(cor)d.cor %>% nrow()[1] 926
d.cor %>% drop_na(target_risk_score, globalScore) %>% nrow()[1] 484
d.cor %>% drop_na(target_risk_score, globalScore) %>%
summarize(cor = tidy(cor.test(target_risk_score, globalScore, method="kendall"))) %>%
unnest(cor)GVC loci annotated with genes in overlaps
GVC ∩ Agora ∩ OpenTargets
gene_ids <- p %>%
filter(..set.. == "GVC∩Agora∩OpenTargets") %>%
unnest(..values..) %>%
select(gene_id = ..values..) %>%
left_join(ago, by = join_by(gene_id == ensembl_gene_id)) %>%
left_join(otensg, by = join_by(gene_id == ensembl_gene_id)) %>%
distinct(gene_id) %>%
pull(gene_id)
length(gene_ids)[1] 75
gvc %>%
filter(gene_id %in% gene_ids) %>%
select(gvc_locus = grouped_loci_gvc, gene_id, gene_symbol) %>%
arrange(gene_symbol) %>%
mutate(gene = gene_symbol) %>%
# unite(gene, gene_id, gene_symbol, sep = ":", remove = FALSE) %>%
distinct(gvc_locus, gene, .keep_all = TRUE) %>%
group_by(gvc_locus) %>%
summarize(genes = str_c(gene, collapse = " | ")) %>%
select(gvc_locus, genes) %>%
gt::gt()| gvc_locus | genes |
|---|---|
| ABCA7 | ABCA7 | NDUFS7 |
| ABI3 / ACE | NGFR | ZNF652 |
| ACE | ACE |
| ADAM10 / MINDY2 | ADAM10 | ALDH1A2 | LIPC |
| ADAMTS4 | ADAMTS4 | FCER1G | NDUFS2 |
| ANK3 / CCDC6 | CCDC6 | SLC16A9 |
| ANKRD31 | ANKRD31 | ENC1 |
| APH1B | LACTB |
| APOE / TOMM40 | APOC1 | APOE | BCAM | MARK4 | NECTIN2 |
| APP | MRPL39 |
| APP / ADAMTS1 | ADAMTS1 |
| BCKDK / KAT8 / VKORC1 | BCKDK | STX4 | VKORC1 |
| BIN1 | BIN1 |
| CASS4 | CASS4 |
| CD2AP | CD2AP |
| CD33 | CD33 |
| CHRNE | ENO3 | RABEP1 | SLC25A11 | ZFP3 |
| CLU / PTK2B | CLU | EPHX2 | PTK2B | SCARA3 |
| CR1 | CR1 |
| CTSH | CTSH |
| DOC2A | DOC2A |
| ECHDC3 / USP6NL | USP6NL |
| EED / PICALM | DLG2 | PICALM |
| EPHA1 / EPHA1-AS1 | EPHA1 |
| HAVCR2 | CYFIP2 | HAVCR2 |
| HLA | HLA-DRA | HLA-DRB1 |
| ICA1 | NXPH1 |
| IDUA | CPLX1 |
| IL34 | MTSS2 |
| INPP5D | INPP5D |
| LILRB2 / TMC4 | LAIR1 |
| MADD / SPI1 | C1QTNF4 | NDUFS3 | NR1H3 | RAPSN | SPI1 |
| MS4A / MS4A2 / MS4A4A / MS4A6A | MRPL16 | MS4A2 | MS4A4A | MS4A6A |
| NDUFAF7 / PRKD3 | QPCT |
| NYAP1 / PILRA / SPDYE3 / ZCWPW1 | NYAP1 |
| OARD1 / TREM2 / TREML2 / UNC5CL | TREM2 |
| PLCG2 | PLCG2 | SDR42E1 |
| PLEKHA1 | HTRA1 |
| RABEP1 / SCIMP | ENO3 | RABEP1 | SLC25A11 | ZFP3 |
| RASGEF1C | MAPK9 |
| RIN3 / SLC24A4 | RIN3 | SLC24A4 |
| SHARPIN | PLEC |
| SIGLEC11 | NR1H2 |
| WNT3 | NSF |
Perform ORA of Agora and OpenTargets genes sorted by global or genetic score
Agora genes sorted by genetics_score
d5 <- ago %>%
drop_na(genetics_score) %>%
arrange(desc(genetics_score))
d5query <- d5 %>% distinct(ensembl_gene_id) %>% pull(ensembl_gene_id)
gostres <- gost(query = query,
organism = "hsapiens",
domain_scope = "annotated",
exclude_iea = TRUE,
ordered_query = TRUE,
significant = TRUE,
user_threshold = 0.005,
correction_method = "fdr")
gostres$result %>% select(term_name, term_id, source, everything())gostplot(gostres, capped = FALSE, interactive = TRUE)Agora genes sorted by target_risk_score
d6 <- ago %>%
drop_na(target_risk_score) %>%
arrange(desc(target_risk_score))
d6query <- d6 %>% distinct(ensembl_gene_id) %>% pull(ensembl_gene_id)
gostres <- gost(query = query,
organism = "hsapiens",
domain_scope = "annotated",
exclude_iea = TRUE,
ordered_query = TRUE,
significant = TRUE,
user_threshold = 0.005,
correction_method = "fdr")
gostres$result %>% select(term_name, term_id, source, everything())gostplot(gostres, capped = FALSE, interactive = TRUE)Agora genes sorted by multi_omics_score
d7 <- ago %>%
drop_na(multi_omics_score) %>%
arrange(desc(multi_omics_score))
d7query <- d7 %>% distinct(ensembl_gene_id) %>% pull(ensembl_gene_id)
gostres <- gost(query = query,
organism = "hsapiens",
domain_scope = "annotated",
exclude_iea = TRUE,
ordered_query = TRUE,
significant = TRUE,
user_threshold = 0.005,
correction_method = "fdr")
gostres$result %>% select(term_name, term_id, source, everything())gostplot(gostres, capped = FALSE, interactive = TRUE)OpenTargets genes sorted by otGeneticsPortal
d8 <- otensg %>%
drop_na(otGeneticsPortal) %>%
arrange(desc(otGeneticsPortal))
d8query <- d8 %>% distinct(ensembl_gene_id) %>% pull(ensembl_gene_id)
gostres <- gost(query = query,
organism = "hsapiens",
domain_scope = "annotated",
exclude_iea = TRUE,
ordered_query = TRUE,
significant = TRUE,
user_threshold = 0.005,
correction_method = "fdr")
gostres$result %>% select(term_name, term_id, source, everything())gostplot(gostres, capped = FALSE, interactive = TRUE)OpenTargets genes sorted by globalScore
d9 <- otensg %>%
drop_na(globalScore) %>%
arrange(desc(globalScore))
d9query <- d9 %>% distinct(ensembl_gene_id) %>% pull(ensembl_gene_id)
gostres <- gost(query = query,
organism = "hsapiens",
domain_scope = "annotated",
exclude_iea = TRUE,
ordered_query = TRUE,
significant = TRUE,
user_threshold = 0.005,
correction_method = "fdr")
gostres$result %>% select(term_name, term_id, source, everything())gostplot(gostres, capped = FALSE, interactive = TRUE)Perform ORA of GVC genes sorted by # of studies
d10 <- read_xlsx("2024-08-29_GVC Table 1C - WORKING COPY.xlsx", sheet = "PG Gene List", skip = 1)
d10 %>% arrange(`PG RANK`)query <-
d10 %>%
rename(gene = `GVC expanded list of possible genes (500kb)`, rank = `PG RANK`) %>%
bind_rows(tibble(gene = "APOE", rank = 0)) %>%
arrange(rank) %>%
distinct(gene) %>%
pull(gene)
gostres <- gost(query = query,
organism = "hsapiens",
domain_scope = "annotated",
exclude_iea = TRUE,
ordered_query = TRUE,
significant = TRUE,
user_threshold = 0.005,
correction_method = "fdr")
gostres$result %>% select(term_name, term_id, source, everything())gostplot(gostres, capped = FALSE, interactive = TRUE)Check missing OpenTargets scores in Brian’s table
t <- read_xlsx("8-23-2024 - GVC Table 1C - WORKING COPYL_MRC.xlsx", skip = 1, na = "No data") |> janitor::clean_names() |> select(gvc_expanded_list_of_possible_genes_500kb, open_target_scores_global, open_target_scores_genetics) |> rename(symbol = gvc_expanded_list_of_possible_genes_500kb)t |>
left_join(ot, by = "symbol") |>
filter(round(open_target_scores_global, 4) != round(globalScore, 4)) |>
select(symbol, open_target_scores_global, globalScore)t |>
left_join(ot, by = "symbol") |>
filter(round(open_target_scores_genetics, 4) != round(otGeneticsPortal, 4)) |>
select(symbol, open_target_scores_genetics, otGeneticsPortal)Print environment
sessioninfo::session_info()─ Session info ───────────────────────────────────────────────────────────────
setting value
version R version 4.4.1 (2024-06-14)
os macOS Sonoma 14.7
system aarch64, darwin20
ui X11
language (EN)
collate en_US.UTF-8
ctype en_US.UTF-8
tz America/New_York
date 2024-10-10
pandoc 3.2 @ /Applications/RStudio.app/Contents/Resources/app/quarto/bin/tools/aarch64/ (via rmarkdown)
─ Packages ───────────────────────────────────────────────────────────────────
package * version date (UTC) lib source
backports 1.5.0 2024-05-23 [1] CRAN (R 4.4.0)
bit 4.5.0 2024-09-20 [1] CRAN (R 4.4.1)
bit64 4.5.2 2024-09-22 [1] CRAN (R 4.4.1)
bitops 1.0-9 2024-10-03 [1] CRAN (R 4.4.1)
broom * 1.0.7 2024-09-26 [1] CRAN (R 4.4.1)
cellranger 1.1.0 2016-07-27 [1] CRAN (R 4.4.0)
cli 3.6.3 2024-06-21 [1] CRAN (R 4.4.0)
colorspace 2.1-1 2024-07-26 [1] CRAN (R 4.4.0)
crayon 1.5.3 2024-06-20 [1] CRAN (R 4.4.0)
crosstalk 1.2.1 2023-11-23 [1] CRAN (R 4.4.0)
data.table 1.16.0 2024-08-27 [1] CRAN (R 4.4.1)
digest 0.6.37 2024-08-19 [1] CRAN (R 4.4.1)
dplyr * 1.1.4 2023-11-17 [1] CRAN (R 4.4.0)
evaluate 1.0.0 2024-09-17 [1] CRAN (R 4.4.1)
fansi 1.0.6 2023-12-08 [1] CRAN (R 4.4.0)
fastmap 1.2.0 2024-05-15 [1] CRAN (R 4.4.0)
forcats * 1.0.0 2023-01-29 [1] CRAN (R 4.4.0)
formatR 1.14 2023-01-17 [1] CRAN (R 4.4.0)
futile.logger * 1.4.3 2016-07-10 [1] CRAN (R 4.4.0)
futile.options 1.0.1 2018-04-20 [1] CRAN (R 4.4.0)
generics 0.1.3 2022-07-05 [1] CRAN (R 4.4.0)
ggplot2 * 3.5.1 2024-04-23 [1] CRAN (R 4.4.0)
glue 1.8.0 2024-09-30 [1] CRAN (R 4.4.1)
gprofiler2 * 0.2.3 2024-02-23 [1] CRAN (R 4.4.0)
gt 0.11.1 2024-10-04 [1] CRAN (R 4.4.1)
gtable 0.3.5 2024-04-22 [1] CRAN (R 4.4.0)
hms 1.1.3 2023-03-21 [1] CRAN (R 4.4.0)
htmltools 0.5.8.1 2024-04-04 [1] CRAN (R 4.4.0)
htmlwidgets 1.6.4 2023-12-06 [1] CRAN (R 4.4.0)
httpuv 1.6.15 2024-03-26 [1] CRAN (R 4.4.0)
httr 1.4.7 2023-08-15 [1] CRAN (R 4.4.0)
janitor * 2.2.0 2023-02-02 [1] CRAN (R 4.4.0)
jsonlite * 1.8.9 2024-09-20 [1] CRAN (R 4.4.1)
knitr 1.48 2024-07-07 [1] CRAN (R 4.4.0)
labeling 0.4.3 2023-08-29 [1] CRAN (R 4.4.0)
lambda.r 1.2.4 2019-09-18 [1] CRAN (R 4.4.0)
later 1.3.2 2023-12-06 [1] CRAN (R 4.4.0)
lazyeval 0.2.2 2019-03-15 [1] CRAN (R 4.4.0)
lifecycle 1.0.4 2023-11-07 [1] CRAN (R 4.4.0)
lubridate * 1.9.3 2023-09-27 [1] CRAN (R 4.4.0)
magrittr 2.0.3 2022-03-30 [1] CRAN (R 4.4.0)
mime 0.12 2021-09-28 [1] CRAN (R 4.4.0)
munsell 0.5.1 2024-04-01 [1] CRAN (R 4.4.0)
pillar 1.9.0 2023-03-22 [1] CRAN (R 4.4.0)
pkgconfig 2.0.3 2019-09-22 [1] CRAN (R 4.4.0)
plotly 4.10.4 2024-01-13 [1] CRAN (R 4.4.0)
promises 1.3.0 2024-04-05 [1] CRAN (R 4.4.0)
purrr * 1.0.2 2023-08-10 [1] CRAN (R 4.4.0)
R6 2.5.1 2021-08-19 [1] CRAN (R 4.4.0)
Rcpp 1.0.13 2024-07-17 [1] CRAN (R 4.4.0)
RCurl 1.98-1.16 2024-07-11 [1] CRAN (R 4.4.0)
readr * 2.1.5 2024-01-10 [1] CRAN (R 4.4.0)
readxl * 1.4.3 2023-07-06 [1] CRAN (R 4.4.0)
rlang 1.1.4 2024-06-04 [1] CRAN (R 4.4.0)
rmarkdown 2.28 2024-08-17 [1] CRAN (R 4.4.0)
rstudioapi 0.16.0 2024-03-24 [1] CRAN (R 4.4.0)
sass 0.4.9 2024-03-15 [1] CRAN (R 4.4.0)
scales 1.3.0 2023-11-28 [1] CRAN (R 4.4.0)
sessioninfo 1.2.2 2021-12-06 [1] CRAN (R 4.4.0)
shiny 1.9.1 2024-08-01 [1] CRAN (R 4.4.0)
snakecase 0.11.1 2023-08-27 [1] CRAN (R 4.4.0)
stringi 1.8.4 2024-05-06 [1] CRAN (R 4.4.0)
stringr * 1.5.1 2023-11-14 [1] CRAN (R 4.4.0)
tibble * 3.2.1 2023-03-20 [1] CRAN (R 4.4.0)
tidyr * 1.3.1 2024-01-24 [1] CRAN (R 4.4.0)
tidyselect 1.2.1 2024-03-11 [1] CRAN (R 4.4.0)
tidyverse * 2.0.0 2023-02-22 [1] CRAN (R 4.4.0)
timechange 0.3.0 2024-01-18 [1] CRAN (R 4.4.0)
tzdb 0.4.0 2023-05-12 [1] CRAN (R 4.4.0)
utf8 1.2.4 2023-10-22 [1] CRAN (R 4.4.0)
vctrs 0.6.5 2023-12-01 [1] CRAN (R 4.4.0)
VennDiagram * 1.7.3 2022-04-12 [1] CRAN (R 4.4.0)
viridisLite 0.4.2 2023-05-02 [1] CRAN (R 4.4.0)
vroom 1.6.5 2023-12-05 [1] CRAN (R 4.4.0)
withr 3.0.1 2024-07-31 [1] CRAN (R 4.4.0)
xfun 0.48 2024-10-03 [1] CRAN (R 4.4.1)
xml2 1.3.6 2023-12-04 [1] CRAN (R 4.4.0)
xtable 1.8-4 2019-04-21 [1] CRAN (R 4.4.0)
yaml 2.3.10 2024-07-26 [1] CRAN (R 4.4.0)
[1] /Users/marcoe02/.Rlib
[2] /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/library
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